1. Field of the Invention
This invention is related to systems and methods of monitoring the condition of civil infrastructure facilities, buildings and other structures, landforms, agriculture and objects of nature as well as environmental monitoring and, in particular, to the autonomous multi-sensor systems capable of functioning for a long period of time and low cost methods of collecting and analyzing data related to the condition of structures, generating and distributing summary information on the condition of the monitored structures.
2. Description of Related Art
Aging of infrastructure and changes in landforms and objects of nature caused by time and environment can result in a wide variety of dangerous and/or undesirable events, including natural disasters and large catastrophes like collapse of structures as well as smaller scale failures of man-made structures, landslides, loss of harvest due to insufficient watering of fields, etc. In many cases these events are result of insufficient information about the condition of structures, landforms, fields, etc. that people responsible for their use and/or maintenance have in their possession.
Civil infrastructure is an area where lack of information can result in large unnecessary expenditures and loss of lives. Aging of civil infrastructure is global multi-trillion dollar problem. Transportation systems (roads, highways, rail systems, ports), utilities (power, communications, water) and public facilities require ever increasing expenditures to maintain their safety, security and integrity (“Civil infrastructure. Advanced sensing technologies and advanced repair materials for the infrastructure: water systems, dams, levees, bridges, roads and highways”, white paper, NIST, March 2009). Each year Federal, state, and local governments spend billions of dollars to upgrade and repair civil infrastructure structures. Federal Highway Administration budget was $41.2 billion in 2008 (U.S. Department of Transportation Fiscal Year 2009 Budget in Brief, http://www.dotgov/bib2009/htm/FHA.html). Federal and state governments spent $70 billion on highway infrastructure and about $30 billion on drinking water and wastewater infrastructure in 2008. Despite these large expenditures there are thousands of structures that require immediate attention and repair. The August 2007 collapse of the I-35W bridge in Minneapolis cost 13 lives and will cause economic losses to the city's economy that are estimated to be close to $200 million. Damaged infrastructure also has large impact on the economy. The American Society of Civil Engineers estimates that $67 billion is spent each year in USA in vehicle repairs caused by poor road conditions (“2008 Status of the nation's highways, bridges and transit conditions and performance”, U.S. Department of transportation, Report to Congress, 2008. www.fhwa.dot.gov/policy/2008 cpr/pdfs/cp2008.pdf and “Report card for America's Infrastructure”, American Society of Civil Engineering, 2009. http://www.infrastructurereportcard.org/sites/default/files/RC2009_full_report.pdf).
A lack of predictability of infrastructure failures is one of the major problems that has to be addressed. It is directly linked to the lack of systems for continuous condition monitoring of civil infrastructure facilities. Aging of civil infrastructure and buildings is a complex process that involves a gradual long-term degradation, wear and can be affected by episodic events like fire, earthquake, flooding, etc. Because the factors affecting the integrity and functionality of engineered structures can not be perfectly predicted, the process of their degradation must be sensed and assessed in some form. Today the most common form of assessing the integrity of a structure is visual inspection sometimes supplemented with other non-qualitative methods. However, the amount of structures that has to be inspected on a regular basis is enormous. Besides buildings, there are about 600 thousand bridges and 4 million miles of public roadway in the USA. It is estimated that about 50 percent of bridges were built before 1940 and about 25 percent of bridges are currently structurally deficient. Beyond the infrequency of inspection, the personnel-intensive inspections are, by their nature, subjective. The inefficiency of current infrastructure condition and quality assessment practices has been studied and reported. For example, in the study of principle bridge inspection methods—the National Bridge Inspection Standards (NBIS), the Federal Highway Administration concluded that the condition ratings that NBIS generates are subjective, highly variable, and not sufficiently reliable for optimal bridge management (“Reliability of visual bridge inspection,” Turner-Fairbank Highway Research Center, Federal Highway Administration, March 2001. http://www.tfhrc.gov/pubrds/marapr01/bridge.htm).
Other countries face similar problems. For example, there are a number of concerns about building safety in Hong Kong where over 50,000 high-rise buildings exceeding 100 meters were built. These buildings are designed for a life cycle of only 50 years and many of them are constructed on steep slopes and reclaimed land making them especially vulnerable to storms and landslides (“Monitoring structural safety in buildings”, cover story, CityU Today, October 2005, pp. 13-17). Singapore provides another example: total of 353 buildings reported to be affected by a 6.6 magnitude earthquake that occurred in Central Sumatra on Mar. 6, 2007 (“Presentation on tremor incidents”, Major C. K. Yuan, Singapore Civil Defense Force, 8 Jun. 2007, http://www.childcarelink.gov.sg/cds/attachments/Tremors.pdf). In both cases condition monitoring of the buildings and components of civil infrastructure is a critical need for assessing condition and optimization of resource allocation as well as preventing failure of the structures.
Technology that could provide more quantitative data on the integrity and condition of civil infrastructure facilities and buildings and can be used in many other areas exists. It is based on use of multi-sensor systems for long-term monitoring of parameters important for functionality and safety of a monitored structure, landform, object of nature, etc. Collecting measurement data over time, analysis of individual sets of data, using accumulated historical data for establishing baselines, detecting both long-term trends and short-term changes of monitored parameters can lead to prevention of catastrophes and much better use of resources.
However, deployment and use of multi-sensor systems can be very expensive and their service life can be much shorter than the service life of a structure or other monitored object. High cost of installation is typical when sensors need to be connected to a remote power source. High cost of maintenance is typical when sensors are powered by batteries and batteries have to be replaced after a relatively short period of time, for example, one year or three years, and also when sensors used in the system are not well protected from environmental factors and can have short service life in the field. High cost of operation and, consequently, high cost of ownership is typical when a multi-sensor system does not have infrastructure to collect and process the data without human involvement. The goal of this invention is to address these problems and provide a low-cost solution for long-term monitoring with help of multi-sensor systems.
One technology that potentially can be used for long-term monitoring of a wide range of structures, landforms, agricultural fields, etc. is wireless sensor networks (WSN). Wireless sensor networks are formed by modules containing both sensors and means for wireless communication. Such modules are often called nodes. Capabilities of this technology have been demonstrated in many projects. For example, Paek et al. from University of South California successfully used a wireless sensor network, named Wisden, for structural health monitoring (SHM) on a large seismic test structure—a full scale model of an actual hospital ceiling (Paek J., Chintalapudi K., Govindan R., Caffrey J., Marsi S. “A wireless sensor network for structural health monitoring: performance and experience”, The 2nd IEEE workshop on embedded networked sensors (EMNETS '05), 2005). Fraiser and Elgamal reported a successful deployment of a WSN-based system for vibration measurements and video monitoring of two bridges near the University of San Diego in 2006 (Fraiser M., Elgamal A. “Video and motion structural monitoring framework”, Proceedings of 4th China-Japan-US Symposium on Structural Control and Monitoring, Oct. 16-17, 2006). However, all of the reported systems are not capable for autonomous work during an extended period of time—longer than few months, they do not provide a convenient interface to a user of the monitored structure and they do not have a method of data collecting and analysis that can reveal long-term trends in parameters of the monitored structures, assess their condition and make recommendations regarding maintenance, repair and replacement of the monitored structures.
Energy supply limits service life of WSN. Best portable batteries available today can power a constantly operating sensor node for only few days. Therefore, the approach assuming that nodes work for only a small period of time making measurements and transmitting data and stay in sleep mode with a very low power consumption most of the time seems to be one of the best practical approaches to development of systems for long-term monitoring of infrastructure facilities, landforms, agricultural fields, objects of nature. With this approach life of nodes can be extended up to several years (“Wireless module claims five year battery life” Electronic Design News, Apr. 7, 2009). For example, a WSN802g module developed by RF Monolithics allows for a data rate up to 2 Mb/s, has current consumption of less than 200 mA in active mode and less than 8 μA in sleep mode, typical transmission range of 50 m indoors and 250 m outdoors. In an example of monitoring civil infrastructure facilities, as a bridge or a building, one can assume that a similar module can be active once a day for 5 minutes only consuming average current of 100 mA and consuming 5 μA for the rest of the day. With these assumptions, current consumption can be evaluated as 8.5 mA*hr per day. A standard AA lithium battery with capacity of 3000 mA*hr can last only for about a year if used to power the module. Using the node less frequently, as for example, once a week can extend its life time to several years. However, it is desirable to combine more frequent monitoring sessions with much longer life time of the nodes.
Energy harvesting is a natural way to provide an additional energy to nodes and extend their service life. Many ways of energy harvesting is being explored, including harvesting of solar energy, thermal energy, mechanical energy, energy of wind, energy of radio waves, energy of radioactive particles and others. A good summary of energy harvesting methods can be found in several reviews (see, for example, Hudak N. S., Amatucci G. G. “Small-scale energy harvesting through thermoelectric, vibration, and radiofrequency power conversion”, Journal of Applied Physics, 103, 101301, 2008). Several energy harvesting devices and materials for use in energy harvesting devices are commercially available as, for example, from Advanced Cerametrics Inc., Perpetuum Ltd., EnOcean Inc., Ferro Solutions Inc., Thermolife Energy Corporation, Smart Material, and some other. Unfortunately, efforts related to combining wireless sensor nodes with energy harvesting devices did not yield any wireless sensing devices capable of working for many years. There are several reasons for that. One is related to a small amount of energy produced by the energy harvesting devices and some loss of this energy due to the need for conditioning of the output voltage of energy harvesting devices to the form suitable for use by the node. The other reason is related to insufficient attention to long-term reliability of the energy harvesting devices and the nodes themselves in field conditions. This resulted in a lack of autonomous systems suitable for monitoring of structures, landforms, agricultural fields and other objects for long periods of time, as 10-15 years and longer, without maintenance.
If a monitoring system is deployed on a structure that has been constructed many years ago then the data collected by the monitoring system can reflect only current condition of the structure, which may have existing structural defects. It may be difficult to assess condition of the structure without a baseline. In most cases, a baseline can be established only as a result of collecting data for some period of time and taking into account dependence of monitored parameters on environmental factors (air temperature, humidity, temperature gradients due to structure heating by sun, wind) and human-related factors (traffic, use of structure, etc.). Although the need for taking into account environmental parameters has been recognized by people working in the area of structural health monitoring, there are no examples of implementation of algorithms taking into account dependence of structural parameters on environmental conditions in establishing a baseline for a monitored structure.
Therefore, there is a need for a technology that can provide low-cost intelligent systems and methods for condition monitoring of structures, landforms, agricultural fields, objects of nature and other objects for very long periods of time—ideally, during the whole life time of the structure. The monitoring system hardware should require minimum maintenance and ideally should work for tens of years without a need for maintenance. The monitoring system should include algorithms for establishing baselines and revealing dependence of monitored parameters on environmental factors.
Being autonomous, such systems should, with minimum or no human involvement, make measurements, collect and analyze measurement data, evaluate condition of the structures, landforms, agricultural fields, and objects of nature under monitoring and provide required data and documentation to their owners and/or companies and agencies responsible for safety, maintenance and repair. In case of sudden and/or significant change of monitored parameters, events that can affect condition of the monitored structures, landforms, agricultural fields, and objects of nature the monitoring systems should provide warnings and allow for real time remote monitoring of parameters and visual inspection of the monitored areas.
This patent application describes wireless multi-sensor systems and supporting infrastructure that can be used for a long-term monitoring of a wide range of structures, landforms, agricultural facilities and fields, for environmental monitoring and for monitoring of objects of nature collectively referred later as “monitored objects” or “objects”. These terms are used later in the patent application referring to a wide variety of long-term monitoring areas.
Different sensors can be used for making periodic measurements related to status, functionality, safety and aging of a monitored object. These sensors can provide very different information that in all cases can be digitized and stored in non-volatile memory. However, the type of information can be very different. In particular, sensors of mechanical parameters—stress, strain, vibration, etc.—can provide data that can be evaluated, for example, after plotting them versus time. Image sensors and web-cameras can provide visual information; microphones can provide audio information, etc. All these types of data will be referred to as “measurement data”.
Collection of measurement data sets acquired during multiple measurement sessions over a period of time that is much longer than the time between the measurement sessions is referred to as “historical data”.
Although the technology can be used in a wide range of applications related to condition monitoring, the primary focus of this patent application is on the condition monitoring of structures or structural health monitoring (SHM).